Tag: CRISP-DM

Data Science projects involve iterative processes and may need changes in data at every iteration. But Data versioning, data pipelines and data workflows make Data Scientist’s life easy, let’s see how.

Analytics is not one time job. It needs to be automated, deployed and improved for future business analytics requirements. Here an IBM expert discusses about development & deployment of analytics assets and capabilities of it.

Many analytic models are not deployed effectively into production while others are not maintained or updated. Applying decision modeling and decision management technology within CRISP-DM addresses this.

The Current State of Automated Machine Learning; The Data Science Puzzle, Revisited; Chatbots on Steroids; Data Science of Sales Calls: 3 Actionable Findings; Four Problems in Using CRISP-DM and How To Fix Them

Many analytic projects fail to understand the business problem they are trying to solve. Correctly applying decision modeling in the Business Understanding phase of CRISP-DM brings clarity to the business problem.

The Data Science Process is a relatively new framework for doing data science. It is compared to previous similar frameworks, and a discussion on process innovation versus repetition is then undertaken.

Traditional methods for the analytical modelling like CRISP-DM have several shortcomings. Here we describe these friction points in CRISP-DM and introduce a new approach of Standard Methodology for Analytics Models which overcomes them.

The second group data mining laws includes: There are always patterns, Data mining amplifies perception in the business domain, Prediction increases information locally by generalisation, Value law, Law of Change. Tom Khabaza explains.

Will Deep Learning take over Machine Learning, make other algorithms obsolete? Cartoon: Halloween Costume for Big Data; CRISP-DM, still the top methodology for analytics, data mining, or data science projects; Big Data accelerates medical research? Or not?

CRISP-DM remains the most popular methodology for analytics, data mining, and data science projects, with 43% share in latest KDnuggets Poll, but a replacement for unmaintained CRISP-DM is long overdue.